{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:16:39Z","timestamp":1743027399910,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031164996"},{"type":"electronic","value":"9783031165009"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16500-9_17","type":"book-chapter","created":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T10:02:31Z","timestamp":1667296951000},"page":"199-207","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Group-Level Affect Recognition in\u00a0Video Using Deviation of\u00a0Frame Features"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6196-0564","authenticated-orcid":false,"given":"Andrey V.","family":"Savchenko","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2776-5471","authenticated-orcid":false,"given":"Lyudmila V.","family":"Savchenko","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8368-537X","authenticated-orcid":false,"given":"Natalya S.","family":"Belova","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,2]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Veltmeijer, E.A., Gerritsen, C., Hindriks, K.: Automatic emotion recognition for groups: a review. IEEE Trans. Affect. Comput. (2021)","DOI":"10.1109\/TAFFC.2021.3065726"},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Sharma, G., Dhall, A., Cai, J.: Audio-visual automatic group affect analysis. IEEE Trans. Affect. Comput. (2021)","DOI":"10.1109\/TAFFC.2021.3104170"},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Pinto, J.R., et al.: Audiovisual classification of group emotion valence using activity recognition networks. In: Proceedings of the 4th International Conference on Image Processing, Applications and Systems (IPAS), pp. 114\u2013119. IEEE (2020)","DOI":"10.1109\/IPAS50080.2020.9334943"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wu, J., Heracleous, P., Wada, S., Kimura, R., Kurihara, S.: Implicit knowledge injectable cross attention audiovisual model for group emotion recognition. In: Proceedings of the ACM International Conference on Multimodal Interaction (ICMI), pp. 827\u2013834 (2020)","DOI":"10.1145\/3382507.3417960"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Sun, M., et al.: Multi-modal fusion using spatio-temporal and static features for group emotion recognition. In: Proceedings of the ACM International Conference on Multimodal Interaction (ICMI), pp. 835\u2013840 (2020)","DOI":"10.1145\/3382507.3417971"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Petrova, A., Vaufreydaz, D., Dessus, P.: Group-level emotion recognition using a unimodal privacy-safe non-individual approach. In: Proceedings of the ACM International Conference on Multimodal Interaction (ICMI), pp. 813\u2013820 (2020)","DOI":"10.1145\/3382507.3417969"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Liu, C., Jiang, W., Wang, M., Tang, T.: Group level audio-video emotion recognition using hybrid networks. In: Proceedings of the ACM International Conference on Multimodal Interaction (ICMI), pp. 807\u2013812 (2020)","DOI":"10.1145\/3382507.3417968"},{"issue":"3","key":"17_CR8","doi-asserted-by":"publisher","first-page":"422","DOI":"10.18287\/2412-6179-2017-41-3-422-430","volume":"41","author":"AV Savchenko","year":"2017","unstructured":"Savchenko, A.V.: Maximum-likelihood dissimilarities in image recognition with deep neural networks. Comput. Opt. 41(3), 422\u2013430 (2017)","journal-title":"Comput. Opt."},{"issue":"2","key":"17_CR9","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1109\/TNNLS.2019.2908973","volume":"31","author":"AV Savchenko","year":"2020","unstructured":"Savchenko, A.V.: Probabilistic neural network with complex exponential activation functions in image recognition. IEEE Trans. Neural Netw. Learn. Syst. 31(2), 651\u2013660 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: Vggface2: a dataset for recognising faces across pose and age. In: Proceedings of International Conference on Automatic Face & Gesture Recognition (FG), pp. 67\u201374. IEEE (2018)","DOI":"10.1109\/FG.2018.00020"},{"issue":"1","key":"17_CR11","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/TAFFC.2017.2740923","volume":"10","author":"A Mollahosseini","year":"2017","unstructured":"Mollahosseini, A., Hasani, B., Mahoor, M.H.: AffectNet: a database for facial expression, valence, and arousal computing in the wild. IEEE Trans. Affect. Comput. 10(1), 18\u201331 (2017)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Savchenko, A.V.: Facial expression and attributes recognition based on multi-task learning of lightweight neural networks. In: Proceedings of the 19th International Symposium on Intelligent Systems and Informatics (SISY), pp. 119\u2013124. IEEE (2021)","DOI":"10.1109\/SISY52375.2021.9582508"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Bargal, S.A., Barsoum, E., Ferrer, C.C., Zhang, C.: Emotion recognition in the wild from videos using images. In: Proceedings of the ACM International Conference on Multimodal Interaction (ICMI), pp. 433\u2013436 (2016)","DOI":"10.1145\/2993148.2997627"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Knyazev, B., Shvetsov, R., Efremova, N., Kuharenko, A.: Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video. arXiv preprint arXiv:1711.04598 (2017)","DOI":"10.1109\/FG.2018.00109"},{"key":"17_CR15","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1007\/978-3-030-87802-3_55","volume-title":"Speech and Computer","author":"L Savchenko","year":"2021","unstructured":"Savchenko, L., V. Savchenko, A.: Speaker-aware training of speech emotion classifier with speaker recognition. In: Karpov, A., Potapova, R. (eds.) SPECOM 2021. LNCS (LNAI), vol. 12997, pp. 614\u2013625. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87802-3_55"},{"key":"17_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1007\/978-3-030-68821-9_25","volume-title":"Pattern Recognition. ICPR International Workshops and Challenges","author":"P Demochkina","year":"2021","unstructured":"Demochkina, P., Savchenko, A.V.: MobileEmotiFace: efficient facial image representations in video-based emotion recognition on mobile devices. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12665, pp. 266\u2013274. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-68821-9_25"},{"key":"17_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/978-3-030-72610-2_18","volume-title":"Analysis of Images, Social Networks and Texts","author":"K Lomotin","year":"2021","unstructured":"Lomotin, K., Makarov, I.: Automated image and video quality assessment for computational video\u00a0editing. In: van der Aalst, W.M.P., et al. (eds.) AIST 2020. LNCS, vol. 12602, pp. 243\u2013256. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72610-2_18"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Zuenko, D., Makarov, I.: Style-transfer autoencoder for efficient deep voice conversation. In: Proceedings of the International Symposium on Computational Intelligence and Informatics (CINTI), pp. 41\u20136. IEEE (2021)","DOI":"10.1109\/CINTI53070.2021.9668528"},{"issue":"7","key":"17_CR19","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1134\/S000511791307014X","volume":"74","author":"AV Savchenko","year":"2013","unstructured":"Savchenko, A.V.: Phonetic words decoding software in the problem of Russian speech recognition. Autom. Remote. Control. 74(7), 1225\u20131232 (2013)","journal-title":"Autom. Remote. Control."},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Eyben, F., W\u00f6llmer, M., Schuller, B.: OpenSMILE: the Munich versatile and fast open-source audio feature extractor. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 1459\u20131462 (2010)","DOI":"10.1145\/1873951.1874246"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Schuller, B., et al.: The INTERSPEECH 2013 computational paralinguistics challenge: Social signals, conflict, emotion, autism. In: Proceedings of 14th Annual Conference of the International Speech Communication Association (INTERSPEECH) (2013)","DOI":"10.21437\/Interspeech.2013-56"},{"issue":"3","key":"17_CR22","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1007\/s11018-019-01617-x","volume":"62","author":"AV Savchenko","year":"2019","unstructured":"Savchenko, A.V., Savchenko, V.V.: A method for measuring the pitch frequency of speech signals for the systems of acoustic speech analysis. Meas. Tech. 62(3), 282\u2013288 (2019)","journal-title":"Meas. Tech."}],"container-title":["Lecture Notes in Computer Science","Analysis of Images, Social Networks and Texts"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16500-9_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T10:06:15Z","timestamp":1667297175000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16500-9_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164996","9783031165009"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16500-9_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"2 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIST","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Analysis of Images, Social Networks and Texts","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tbilisi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Georgia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aist2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aistconf.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"118","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"20","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.79","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Out of the 118 submission, 26 were rejected before being sent to peer review.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}